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add level2 ops (apache#6)
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@piiswrong @tqchen
piiswrong authored and tqchen committed May 29, 2018
1 parent e2ae388 commit 31eb2c5
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153 changes: 153 additions & 0 deletions nnvm/include/nnvm/top/nn.h
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,20 @@
namespace nnvm {
namespace top {

enum LayoutFlag {
kNCHW = 0,
kNHWC,
kCHWN,

kNCW = 1 << 3,
kNWC,
kCWN,

kNCDHW = 1 << 5,
kNDHWC,
kCDHWN
};

struct DenseParam : public dmlc::Parameter<DenseParam> {
int units;
bool use_bias;
Expand Down Expand Up @@ -85,6 +99,145 @@ struct LogSoftmaxParam : public dmlc::Parameter<LogSoftmaxParam> {
}
};

struct Conv2DParam : public dmlc::Parameter<Conv2DParam> {
int channels;
TShape kernel_size;
TShape strides;
TShape padding;
TShape dilation;
int groups;
int layout;
bool use_bias;

DMLC_DECLARE_PARAMETER(Conv2DParam) {
DMLC_DECLARE_FIELD(channels)
.describe("The dimensionality of the output space"
"i.e. the number of output channels in the convolution.");
DMLC_DECLARE_FIELD(kernel_size)
.describe("Specifies the dimensions of the convolution window.");
DMLC_DECLARE_FIELD(strides).set_default(TShape({1, 1}))
.describe("Specifies the strides of the convolution.");
DMLC_DECLARE_FIELD(padding).set_default(TShape({0, 0}))
.describe("If padding is non-zero, then the input is implicitly zero-padded"
"on both sides for padding number of points");
DMLC_DECLARE_FIELD(dilation).set_default(TShape({1, 1}))
.describe("Specifies the dilation rate to use for dilated convolution.");
DMLC_DECLARE_FIELD(groups).set_default(1)
.describe("Controls the connections between inputs and outputs."
"At groups=1, all inputs are convolved to all outputs."
"At groups=2, the operation becomes equivalent to having two convolution"
"layers side by side, each seeing half the input channels, and producing"
"half the output channels, and both subsequently concatenated.");
DMLC_DECLARE_FIELD(layout)
.add_enum("NCHW", kNCHW)
.add_enum("NHWC", kNHWC)
.set_default(kNCHW)
.describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
"'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
"dimensions respectively. Convolution is applied on the 'H' and"
"'W' dimensions.");
DMLC_DECLARE_FIELD(use_bias).set_default(true)
.describe("Whether the layer uses a bias vector.");
}
};


struct Conv2DTransposeParam : public dmlc::Parameter<Conv2DTransposeParam> {
int channels;
TShape kernel_size;
TShape strides;
TShape padding;
TShape output_padding;
TShape dilation;
int groups;
int layout;
bool use_bias;

DMLC_DECLARE_PARAMETER(Conv2DTransposeParam) {
DMLC_DECLARE_FIELD(channels)
.describe("The dimensionality of the output space"
"i.e. the number of output channels in the convolution.");
DMLC_DECLARE_FIELD(kernel_size)
.describe("Specifies the dimensions of the convolution window.");
DMLC_DECLARE_FIELD(strides).set_default(TShape({1, 1}))
.describe("Specifies the strides of the convolution.");
DMLC_DECLARE_FIELD(output_padding).set_default(TShape({0, 0}))
.describe("Zero-padding added to one side of the output.");
DMLC_DECLARE_FIELD(padding).set_default(TShape({0, 0}))
.describe("If padding is non-zero, then the input is implicitly zero-padded"
"on both sides for padding number of points");
DMLC_DECLARE_FIELD(dilation).set_default(TShape({1, 1}))
.describe("Specifies the dilation rate to use for dilated convolution.");
DMLC_DECLARE_FIELD(groups).set_default(1)
.describe("Controls the connections between inputs and outputs."
"At groups=1, all inputs are convolved to all outputs."
"At groups=2, the operation becomes equivalent to having two convolution"
"layers side by side, each seeing half the input channels, and producing"
"half the output channels, and both subsequently concatenated.");
DMLC_DECLARE_FIELD(layout)
.add_enum("NCHW", kNCHW)
.add_enum("NHWC", kNHWC)
.set_default(kNCHW)
.describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
"'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
"dimensions respectively. Convolution is applied on the 'H' and"
"'W' dimensions.");
DMLC_DECLARE_FIELD(use_bias).set_default(true)
.describe("Whether the layer uses a bias vector.");
}
};

struct Pool2DParam : public dmlc::Parameter<Pool2DParam> {
TShape pool_size;
TShape strides;
TShape padding;
int groups;
int layout;
bool ceil_mode;

DMLC_DECLARE_PARAMETER(Pool2DParam) {
DMLC_DECLARE_FIELD(pool_size)
.describe("Size of the pooling windows..");
DMLC_DECLARE_FIELD(strides).set_default(TShape({1, 1}))
.describe("Specifies the strides of the convolution.");
DMLC_DECLARE_FIELD(padding).set_default(TShape({0, 0}))
.describe("If padding is non-zero, then the input is implicitly zero-padded"
"on both sides for padding number of points");
DMLC_DECLARE_FIELD(groups).set_default(1)
.describe("Controls the connections between inputs and outputs."
"At groups=1, all inputs are convolved to all outputs."
"At groups=2, the operation becomes equivalent to having two convolution"
"layers side by side, each seeing half the input channels, and producing"
"half the output channels, and both subsequently concatenated.");
DMLC_DECLARE_FIELD(layout)
.add_enum("NCHW", kNCHW)
.add_enum("NHWC", kNHWC)
.set_default(kNCHW)
.describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
"'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
"dimensions respectively. Convolution is applied on the 'H' and"
"'W' dimensions.");
DMLC_DECLARE_FIELD(ceil_mode).set_default(false)
.describe("When true, will use ceil instead of floor to compute the output shape.");
}
};


struct GlobalPool2DParam : public dmlc::Parameter<GlobalPool2DParam> {
int layout;

DMLC_DECLARE_PARAMETER(GlobalPool2DParam) {
DMLC_DECLARE_FIELD(layout)
.add_enum("NCHW", kNCHW)
.add_enum("NHWC", kNHWC)
.set_default(kNCHW)
.describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
"'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
"dimensions respectively. Convolution is applied on the 'H' and"
"'W' dimensions.");
}
};

} // namespace top
} // namespace nnvm

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